Bouzinier michael (9 resultados)
Research Data that Can be Trusted
Bouzinier, Michael; Etin, Dmitry; Khoshnevis, Naeem; Shad, Max; Yockel, Scott
Idioma: Inglés
Editorial: Springer, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 82,29
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Condición: New.
Research Data That Can Be Trusted
Bouzinier, Michael Etin, Dmitry Khoshnevis, Naeem Shad, Max Yockel, Scott
Idioma: Inglés
Editorial: Springer Nature Switzerland Ag, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 78,78
Envío por EUR 11,73Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: Brand New. In Stock.
Idioma: Inglés
Editorial: Springer, Berlin, Springer Nature Switzerland, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 58,39
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - In this book we argue for the need for a new approach to data provenance and explain how recent advancements in data processing workflow automation present an opportunity to address this need. We introduce descriptive dataflow operators - a novel a…pproach based on integrating descriptive workflow languages with a data modeling Domain-Specific Language (DSL). We review the current workflow automation technologies and propose a DSL that supports complex data transformations, enhances reproducibility, and enables precise data lineage tracking. Within the framework we introduce the concept of descriptive dataflow operators for more flexible and expressive data transformations.Modern healthcare increasingly relies on complex data pipelines to process diverse diagnostic information, clinical records, and research data. This growing complexity, combined with emerging AI/ML applications and stricter regulatory oversight, demands sophisticated approaches to data preparation, documentation, and validation. Healthcare organizations face mounting pressure to ensure granular traceability and reproducibility of their data transformations while maintaining regulatory compliance. These challenges are particularly acute in research settings, where data provenance and quality validation become critical for scientific reproducibility and regulatory adherence.Given the increasing complexity of healthcare data, data ingestion and transformation workflows present significant technical challenges, particularly in ensuring the reproducibility and seamless integration of diverse datasets for ML and AI model development.We introduce the Dorieh Data Platform as an exemplar implementation of a DSL, providing a comprehensive framework for reproducible research. The platform's infrastructure supports robust data lineage documentation, validation, and error logging, making it a powerful tool for healthcare data analysis by ensuring transparent, auditable data processes and regulatory conformance.We show how to apply this framework to analyze healthcare claims data quality, revealing insights into inconsistencies and deficiencies. Our approach demonstrates the potential for improved data management and accountability in scientific research, underscoring the necessity for precise, reproducible data transformation methodologies to produce reliable research outcomes.
Idioma: Inglés
Editorial: Springer, Berlin, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 50,40
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Research Data that Can Be Trusted | Michael Bouzinier (u. a.) | Taschenbuch | xxi | Englisch | 2026 | Springer, Berlin | EAN 9783032210319 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Editorial: Springer, Berlin, Springer Nature Switzerland Jun 2026, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
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- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 53,49
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book we argue for the need for a new approach to data provenance and explain how recent advancements in data processing workflow automation present an opportunity to address this need. We introduce descriptive dataflow opera…tors - a novel approach based on integrating descriptive workflow languages with a data modeling Domain-Specific Language (DSL). We review the current workflow automation technologies and propose a DSL that supports complex data transformations, enhances reproducibility, and enables precise data lineage tracking. Within the framework we introduce the concept of descriptive dataflow operators for more flexible and expressive data transformations.Modern healthcare increasingly relies on complex data pipelines to process diverse diagnostic information, clinical records, and research data. This growing complexity, combined with emerging AI/ML applications and stricter regulatory oversight, demands sophisticated approaches to data preparation, documentation, and validation. Healthcare organizations face mounting pressure to ensure granular traceability and reproducibility of their data transformations while maintaining regulatory compliance. These challenges are particularly acute in research settings, where data provenance and quality validation become critical for scientific reproducibility and regulatory adherence.Given the increasing complexity of healthcare data, data ingestion and transformation workflows present significant technical challenges, particularly in ensuring the reproducibility and seamless integration of diverse datasets for ML and AI model development.We introduce the Dorieh Data Platform as an exemplar implementation of a DSL, providing a comprehensive framework for reproducible research. The platform's infrastructure supports robust data lineage documentation, validation, and error logging, making it a powerful tool for healthcare data analysis by ensuring transparent, auditable data processes and regulatory conformance.We show how to apply this framework to analyze healthcare claims data quality, revealing insights into inconsistencies and deficiencies. Our approach demonstrates the potential for improved data management and accountability in scientific research, underscoring the necessity for precise, reproducible data transformation methodologies to produce reliable research outcomes. 202 pp. Englisch.
Research Data that Can be Trusted
Bouzinier, Michael; Etin, Dmitry; Khoshnevis, Naeem; Shad, Max; Yockel, Scott
Idioma: Inglés
Editorial: Springer, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
- Tapa blanda
- Impresión bajo demanda
Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 82,11
Envío por EUR 7,63Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
Research Data that Can be Trusted
Bouzinier, Michael; Etin, Dmitry; Khoshnevis, Naeem; Shad, Max; Yockel, Scott
Idioma: Inglés
Editorial: Springer, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
- Tapa blanda
- Impresión bajo demanda
Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 83,04
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
Research Data that Can be Trusted
Bouzinier, Michael; Etin, Dmitry; Khoshnevis, Naeem; Shad, Max; Yockel, Scott
Idioma: Inglés
Editorial: Springer Verlag GmbH, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 48,37
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Kartoniert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Idioma: Inglés
Editorial: Springer Verlag Gmbh Jun 2026, 2026
Serie: SpringerBriefs in Computer Science, Libro 101 de 60. Libro 101 de 60 - SpringerBriefs in Computer Science
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 53,49
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book we argue for the need for a new approach to data provenance and explain how recent advancements in data processing workflow automation present an opportunity to address this need. We introduce descriptive dataflow operators… - a novel approach based on integrating descriptive workflow languages with a data modeling Domain-Specific Language (DSL). We review the current workflow automation technologies and propose a DSL that supports complex data transformations, enhances reproducibility, and enables precise data lineage tracking. Within the framework we introduce the concept of descriptive dataflow operators for more flexible and expressive data transformations. Modern healthcare increasingly relies on complex data pipelines to process diverse diagnostic information, clinical records, and research data. This growing complexity, combined with emerging AI/ML applications and stricter regulatory oversight, demands sophisticated approaches to data preparation, documentation, and validation. Healthcare organizations face mounting pressure to ensure granular traceability and reproducibility of their data transformations while maintaining regulatory compliance. These challenges are particularly acute in research settings, where data provenance and quality validation become critical for scientific reproducibility and regulatory adherence. Given the increasing complexity of healthcare data, data ingestion and transformation workflows present significant technical challenges, particularly in ensuring the reproducibility and seamless integration of diverse datasets for ML and AI model development. We introduce the Dorieh Data Platform as an exemplar implementation of a DSL, providing a comprehensive framework for reproducible research. The platform's infrastructure supports robust data lineage documentation, validation, and error logging, making it a powerful tool for healthcare data analysis by ensuring transparent, auditable data processes and regulatory conformance. We show how to apply this framework to analyze healthcare claims data quality, revealing insights into inconsistencies and deficiencies. Our approach demonstrates the potential for improved data management and accountability in scientific research, underscoring the necessity for precise, reproducible data transformation methodologies to produce reliable research outcomes.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg Englisch.
